• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.837-850
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• 2002

In this paper, we propose an EEG-based response time prediction method during a yes/no cognitive decision task. In the experimental task, a subject goes through responding of visual stimulus, understanding the given problem, controlling hand motions, and hitting a key. Considering the subject's varying brain activities, we model subjects' mental states with defining CT (cut time), ST (selection time), and RP (repeated period). Based on the assumption between ST and RT in the mental model, we predict subjects' response time by detection of selection time. To recognize the subjects' selection time ST, we extract 3 types of feature from the filtered brain waves at frequency bands of $\alpha$, $\beta$, ${\gamma}$ waves in 4 electrode pairs combined by spatial relationships. From the extracted features, we construct specific rules for each subject and meta rules including common factors in all subjects. Applying the ST detection rules to 8 subjects gives 83% success rates and also shows that the subjects will hit a key in 0.73 seconds after ST detected. To validate the detection rules and parameters, we test the rules for 2 subjects among 8 and discuss about the experimental results. We expect that the proposed detection method can be a basic technology for brain-computer-interface by combining with left/right hand movement or yes/no discrimination methods.

• The KIPS Transactions:PartB
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• v.16B no.3
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• pp.195-202
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• 2009

In this paper, we propose the regions segmentation method of the white matter and the gray matter for brain MR image by using the ant colony optimization algorithm. Ant Colony Optimization (ACO) is a new meta heuristics algorithm to solve hard combinatorial optimization problem. This algorithm finds the expected pixel for image as the real ant finds the food from nest to food source. Then ants deposit pheromone on the pixels, and the pheromone will affect the motion of next ants. At each iteration step, ants will change their positions in the image according to the transition rule. Finally, we can obtain the segmentation results through analyzing the pheromone distribution in the image. We compared the proposed method with other threshold methods, viz. the Otsu' method, the genetic algorithm, the fuzzy method, and the original ant colony optimization algorithm. From comparison results, the proposed method is more exact than other threshold methods for the segmentation of specific region structures in MR brain image.

• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.123-132
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• 2005

A version control system is used in a rapidly changed environment or a program which developed in a complicated environment. And configuration thread information supporting and it's processing method has an important part in version control. Configuration thread tool such as a system model of DSEE, a view of ClearCase, a label of SourceSafe, and the package of CCC/Harvest have applied to formalized configuration rule by user and obtained a desired configuration information of the version. But it is a problem of configuration thread in supporting information that we. in this method. can't know a exactly well-defined configuration rule information and a predefined information. And these information have a demerit that can't supported the close connection along with undefined version and a meta-information. In this paper. we have modeling a system for these problems to solve and a efficiently configuration thread supported. We also proposed a mixed retrieval model included a boolean retrieval model and a vector retrieval model for support efficiently configuration thread information. We applied and designed the libraries using extended facet method.

• Journal of the Korea Society of Computer and Information
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• v.14 no.1
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• pp.9-15
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• 2009

Ant Colony System is new meta heuristic for hard combinatorial optimization problem. The original ant colony system accomplishes a pheromone updating about only the global optimal path using global updating rule. But, If the global optimal path is not searched until the end condition is satisfied, only pheromone evaporation happens to no matter how a lot of iteration accomplishment. In this paper, the length of the global optimal path does not improved within the limited iterations, we evaluates this state that fall into the local optimum and selects the next node using changed parameters in the state transition rule. This method has effectiveness of the search for a path through diversifications is enhanced by decreasing the value of parameter of the state transition rules for the select of next node, and escape from the local optima is possible. Finally, the performance of Best and Average_Best of proposed algorithm outperforms original ACS.

• Journal of KIISE
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• v.41 no.9
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• pp.674-685
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• 2014

The Knowledge service system needs to infer a new knowledge from indicated knowledge to provide its effective service. Most of the Knowledge service system is expressed in terms of ontology. The volume of knowledge information in a real world is getting massive, so effective technique for massive data of ontology is drawing attention. This paper is to provide the method to infer massive data-ontology to the extent of RDFS, based on cloud computing environment, and evaluate its capability. RDFS inference suggested in this paper is focused on both the method applying MapReduce based on RDFS meta table, and the method of single use of cloud computing memory without using MapReduce under distributed file computing environment. Therefore, this paper explains basically the inference system structure of each technique, the meta table set-up according to RDFS inference rule, and the algorithm of inference strategy. In order to evaluate suggested method in this paper, we perform experiment with LUBM set which is formal data to evaluate ontology inference and search speed. In case LUBM6000, the RDFS inference technique based on meta table had required 13.75 minutes(inferring 1,042 triples per second) to conduct total inference, whereas the method applying the cloud computing memory had needed 7.24 minutes(inferring 1,979 triples per second) showing its speed twice faster.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.5
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• pp.972-977
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• 2003

XML has weakness problems on document modulation and elimination of data Because of the XML gives priority to present data format, XML signature, XML encryption, or XML access control is provided to overcome those weakness problems. However, structured XML efficiency contravention problem occurred from XML encryption and absence of protection from DID attack are still remains unsolved. In this paper, we suggests the XML schema that satisfies both validity and encryption. The DTD is unnecessary because XML schema supports Well-Formed XML documents and include meta information. Also XML schema has possibility to generate each XML document dynamically and because of self efficiency investigator rule, it has an advantage on extendability of DID based encryption of XML documents.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.1
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• pp.93-101
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• 2010

Currently lots of studies to solve meta-data interoperability in between schema attributes are conducted. But the accuracy in previous schema mapping studies is low since the studies just use the similarity in between attributes. So the studies are not suitable for the schema mapping such as document conversion, system integration, etc. In this paper, we propose a method which can conduct the schema mapping interactively using frequent pattern mining. The method can conduct more accurate mapping process because the method use the description element which is an element among each schema element for the metadata standard. A performance study has been conducted to compare the accuracy performance of the method using metadata standards.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.934-937
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• 2012

Since a migration method of the mobile agent is a factor that affects the overall performance of the entire distributed system, it is necessary to find efficient migration methods of the mobile agent within the sensor network using the various metadata related on the sensor network. Accordingly, this paper proposes the directional(forward and backward) migration methods of an active rule mobile agent applying the naming method that used the information of the meta_table. The results of the various experiments in this study present the efficacy of mobile agent middleware system and the possibility of constructing efficient sensor network application environment.

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.59-66
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• 2010

We present a personalized clothing recommender system - one that mines association rules from transaction described in ontologies and infers a recommendation from the rules. The recommender system can forecast frequently changing trends of clothing using the Onto-Apriori algorithm, and it makes appropriate recommendations for each users possible through the inference marked as meta nodes. We simulates the rule generator and the inferential search engine of the system with focus on accuracy and efficiency, and our results validate the system.

• Journal of the Korean Society of Systems Engineering
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• v.19 no.2
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• pp.18-31
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• 2023

Exploring artificial intelligence and machine learning for nuclear safety has witnessed increased interest in recent years. To contribute to this area of research, a machine learning model capable of accurately predicting nuclear power plant response with minimal computational cost is proposed. To develop a robust machine learning model, the Best Estimate Plus Uncertainty (BEPU) approach was used to generate a database to train three models and select the best of the three. The BEPU analysis was performed by coupling Dakota platform with the best estimate thermal hydraulics code RELAP/SCDAPSIM/MOD 3.4. The Code Scaling Applicability and Uncertainty approach was adopted, along with Wilks' theorem to obtain a statistically representative sample that satisfies the USNRC 95/95 rule with 95% probability and 95% confidence level. The generated database was used to train three models based on Recurrent Neural Networks; specifically, Long Short-Term Memory, Gated Recurrent Unit, and a hybrid model with Long Short-Term Memory coupled to Convolutional Neural Network. In this paper, the System Engineering approach was utilized to identify requirements, stakeholders, and functional and physical architecture to develop this project and ensure success in verification and validation activities necessary to ensure the efficient development of ML meta-models capable of predicting of the nuclear power plant response.